Modeling and predicting spring land surface phenology of the deciduous broadleaf forest in northern China

Using normalized difference vegetation index data, we fitted unified forcing and chilling phenology models to start date of the growing season (SOS) during 1982 to 2006 and selected optimum models for each pixel. Then, we validated performances of optimum models in extrapolating SOS dates during 2007 to 2011. Moreover, we reconstructed SOS time series over 1950 to 2005 and predicted decadal mean anomalies of SOS dates over 2006 to 2100 under scenarios of the representative concentration pathway (RCP) 4.5 and 8.5. Finally, we compared structures and parameters of the satellite data-based phenology model with those of the ground-based leaf unfolding model. Results show that the unified forcing model has higher simulation parsimony and efficiency than the unified chilling model at 96.4% of pixels. The external validation confirmed feasibility of the unified forcing model in predicting the SOS date. Across the entire region, predicted SOS dates indicate significant advancements by 0.65–1.79 days per decade. Taking the mean SOS date during 1961 to 1990 as the reference value, the absolute values of decadal mean anomalies of the SOS date represent an overall increase with the time. Consequently, the average SOS date during 2091 to 2100 would be 11.8–20.0 days earlier than the average SOS date during 1961 to 1990. At pixel scales, the absolute values of decadal mean anomalies of the SOS date under the RCP 8.5 scenario display mostly a latitudinal gradient from south to north. Similar distribution ranges of parameter d and e of the ground-based and satellite data-based phenology models demonstrate that the satellite-derived SOS date has consistent response to air temperature with the leaf unfolding date of dominant deciduous trees, which implies that the predicted start of the growing season may to some extent reflect changing tendency of the ground leafing phenology in the 21st century.

[1]  Ranga B. Myneni,et al.  Assessing Performance of NDVI and NDVI3g in Monitoring Leaf Unfolding Dates of the Deciduous Broadleaf Forest in Northern China , 2013, Remote. Sens..

[2]  T. A. Black,et al.  Climatic and Phenological Controls of the Carbon and Energy Balances of Three Contrasting Boreal Forest Ecosystems in Western Canada , 2009 .

[3]  I. Chuine,et al.  A unified model for budburst of trees. , 2000, Journal of theoretical biology.

[4]  Xiangming Xiao,et al.  Spatial analysis of growing season length control over net ecosystem exchange , 2005 .

[5]  H. Akaike,et al.  Information Theory and an Extension of the Maximum Likelihood Principle , 1973 .

[6]  John F. Mustard,et al.  Phenology model from surface meteorology does not capture satellite‐based greenup estimations , 2007 .

[7]  S. Running,et al.  A continental phenology model for monitoring vegetation responses to interannual climatic variability , 1997 .

[8]  Ivan A. Janssens,et al.  Bayesian comparison of six different temperature-based budburst models for four temperate tree species , 2012 .

[9]  David Medvigy,et al.  Predicting changes in temperate forest budburst using continental‐scale observations and models , 2013 .

[10]  Michael D. Dettinger,et al.  Implementing a U.S. national phenology network , 2005 .

[11]  D. Baldocchi.  ‘Breathing’ of the terrestrial biosphere: lessons learned from a global network of carbon dioxide flux measurement systems , 2008 .

[12]  Heikki Hänninen,et al.  Modelling bud dormancy release in trees from cool and temperate regions. , 1990 .

[13]  H. Hänninen.  Does climatic warming increase the risk of frost damage in northern trees , 1991 .

[14]  Xiaoqiu Chen,et al.  Regional unified model‐based leaf unfolding prediction from 1960 to 2009 across northern China , 2013, Global change biology.

[15]  J. Edmonds,et al.  RCP4.5: a pathway for stabilization of radiative forcing by 2100 , 2011 .

[16]  Jin Chen,et al.  A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter , 2004 .

[17]  Pierre Friedlingstein,et al.  A global prognostic scheme of leaf onset using satellite data , 2000 .

[18]  P. Ciais,et al.  Net carbon dioxide losses of northern ecosystems in response to autumn warming , 2008, Nature.

[19]  G. Mohren,et al.  Sensitivity of FORGRO to climatic change scenarios: A case study on Betula pubescens, Fagus sylvatica and Quercus robur in The Netherlands , 1996 .

[20]  G. Robertson.  A biometeorological time scale for a cereal crop involving day and night temperatures and photoperiod , 1968 .

[21]  J. Nash,et al.  River flow forecasting through conceptual models part I — A discussion of principles☆ , 1970 .

[22]  Michele Meroni,et al.  Remote sensing of larch phenological cycle and analysis of relationships with climate in the Alpine region , 2010 .

[23]  Denis-Didier Rousseau,et al.  Selecting models to predict the timing of flowering of temperate trees: implications for tree phenology modelling , 1999 .

[24]  Isabelle Chuine,et al.  Leaf phenology in 22 North American tree species during the 21st century , 2009 .

[25]  Dan Tarpley,et al.  Diverse responses of vegetation phenology to a warming climate , 2007 .

[26]  K. Kramer.  Modelling comparison to evaluate the importance of phenology for the effects of climate change on growth of temperate-zone deciduous trees , 1995 .

[27]  Sylvain Delzon,et al.  Assessing the effects of climate change on the phenology of European temperate trees , 2011 .

[28]  Y. Xue,et al.  Terrestrial biosphere models need better representation of vegetation phenology: results from the North American Carbon Program Site Synthesis , 2012 .

[29]  K. E. Moore,et al.  Seasonal Variation in Radiative and Turbulent Exchange at a Deciduous Forest in Central Massachusetts , 1996 .

[30]  Geoffrey M. Henebry,et al.  Trend analysis of the Pathfinder AVHRR Land (PAL) NDVI data for the deserts of central Asia , 2004, IEEE Geoscience and Remote Sensing Letters.

[31]  Lin Xu,et al.  Phenological responses of Ulmus pumila (Siberian Elm) to climate change in the temperate zone of China , 2012, International Journal of Biometeorology.

[32]  Jianwu Tang,et al.  Regional-scale phenology modeling based on meteorological records and remote sensing observations , 2012 .

[33]  Koen Kramer,et al.  Selecting a model to predict the onset of growth of Fagus sylvatica , 1994 .

[34]  F. Giorgi,et al.  Addressing climate information needs at the regional level: the CORDEX framework , 2009 .

[35]  Corinne Le Quéré,et al.  Climate Change 2013: The Physical Science Basis , 2013 .

[36]  Xiaoqiu Chen,et al.  Modeling greenup date of dominant grass species in the Inner Mongolian Grassland using air temperature and precipitation data , 2014, International Journal of Biometeorology.

[37]  C. van Leeuwen,et al.  General phenological model to characterise the timing of flowering and veraison of Vitis vinifera L. , 2011 .

[38]  Mirco Migliavacca,et al.  On the uncertainty of phenological responses to climate change, and implications for a terrestrial biosphere model , 2012 .

[39]  Dennis D. Baldocchi,et al.  Seasonal and interannual variability of energy fluxes over a broadleaved temperate deciduous forest in North America , 2000 .

[40]  Xiaoqiu Chen,et al.  Temperature controls on the spatial pattern of tree phenology in China's temperate zone , 2012 .

[41]  Xiaoqiu Chen,et al.  Comparison of spatial patterns of satellite-derived and ground-based phenology for the deciduous broadleaf forest of China , 2013 .

[42]  J. Townshend,et al.  Global land cover classi(cid:142) cation at 1 km spatial resolution using a classi(cid:142) cation tree approach , 2004 .

[43]  H. Hänninen,et al.  Effect of increased winter temperature on the onset of height growth of Scots pine: a field test of a phenological model. , 1993 .

[44]  Risto Sarvas,et al.  Investigations on the annual cycle of development of forest trees. II. Autumn dormancy and winter dormancy , 1974 .

[45]  Denis-Didier Rousseau,et al.  Fitting models predicting dates of flowering of temperate‐zone trees using simulated annealing , 1998 .

[46]  Takeshi Ohta,et al.  Climate change and extension of the Ginkgo biloba L. growing season in Japan , 2003 .

[47]  Andrew D. Richardson,et al.  Phenological Differences Between Understory and Overstory: A Case Study Using the Long-Term Harvard Forest Records , 2009 .

[48]  Heikki Hänninen,et al.  Effects of climatic change on trees from cool and temperate regions: an ecophysiological approach to modelling of bud burst phenology , 1995 .

[49]  M. Lechowicz,et al.  Predicting the timing of budburst in temperate trees , 1992 .

[50]  Ankur R. Desai,et al.  The Phenology of Gross Ecosystem Productivity and Ecosystem Respiration in Temperate Hardwood and Conifer Chronosequences , 2009 .

[51]  P. Hari,et al.  The implications of geographical variation in climate for differentiation of bud dormancy ecotypes in Scots pine , 1996 .

[52]  N. Nakicenovic,et al.  RCP 8.5—A scenario of comparatively high greenhouse gas emissions , 2011 .

[53]  I. Chuine,et al.  Scaling phenology from the local to the regional level: advances from species‐specific phenological models , 2000 .

[54]  Thuy Le Toan,et al.  Bud‐burst modelling in Siberia and its impact on quantifying the carbon budget , 2005, Global change biology.

[55]  Alessandro Anav,et al.  Global Data Sets of Vegetation Leaf Area Index (LAI)3g and Fraction of Photosynthetically Active Radiation (FPAR)3g Derived from Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) for the Period 1981 to 2011 , 2013, Remote. Sens..

[56]  Effects of climatic warming on northern trees: Testing the frost damage hypothesis with meteorological data from provenance transfer experiments , 1996 .

[57]  Sietse O. Los,et al.  Predicting the time of green up in temperate and boreal biomes , 2011 .

[58]  Julien Boé,et al.  Modelling interannual and spatial variability of leaf senescence for three deciduous tree species in France. , 2009 .

[59]  K. Kramer.  A modelling analysis of the effects of climatic warming on the probability of spring frost damage to tree species in the Netherlands and Germany , 1994 .

[60]  M. Cannell,et al.  Date of budburst of fifteen tree species in Britain following climatic warming , 1989 .

[61]  J. Townshend,et al.  Global land cover classifications at 8 km spatial resolution: The use of training data derived from Landsat imagery in decision tree classifiers , 1998 .

[62]  M. Cannell,et al.  Thermal time, chill days and prediction of budburst in Picea sitchensis , 1983 .

[63]  M. Cannell,et al.  CLIMATIC WARMING, SPRING BUDBURST AND FROST DAMAGE ON TREES , 1986 .

[64]  Isabelle Chuine,et al.  Sensitivity analysis of the tree distribution model Phenofit to climatic input characteristics: implications for climate impact assessment , 2005 .

[65]  Robert W. Christopherson.  Geosystems: An Introduction to Physical Geography , 1992 .

[66]  Tapio Linkosalo,et al.  A comparison of phenological models of leaf bud burst and flowering of boreal trees using independent observations. , 2008, Tree physiology.